Cannonball Data Cleaning and Diagnostics

Data Check Plans - For Michael Discussion 8/26

This markdown will hopefully accomplish the following data cleaning and diagnostic checks

1. Import and clean data:
   1. top chunk and tidy_cannon function
2. identify variables to transform
   2. (set_vars) and create create_vars
3. look for irregular rows:
   1. hits that should be misses, misses that should be hits
   2. systematically weird variation (rts that are too long, too short)
4. Create summary table and highlight problematic rows
   1. High percentage of no_resp
   2. low percentage of hits
5. Check RTs and provide info on possible transforms
   1. histograms
   2. means
6. Graphical representation of trials
   1. PE_raw by hits/misses
   2. PE_angmu by hits/misses

Adaptive Learning Task (ADLT)

In the cannon task, participants are instructed to place a shield at any point around a circle (Nassar et al., 2019). This shield covers only a portion of the 360-degree circle. The goal of the task is to infer where a cannonball might hit along the circle based on the information gathered from previous cannonball strikes and place the shield at those locations. Before each block of the task, the generative structure of the block is fully explained to the participants via written instructions and practice trials of the task. During the practice trials, participants can see where the cannon is aimed and use that information to place their shield. The cannon could theoretically hit anywhere within a 10-degree range from the center of the cannon’s muzzle. After this practice phase, participants complete the primary experiment where the cannon is removed and they must infer the cannon’s aim. The pattern of the cannon’s aim can either (1) change slightly within a specified range of the circle, with “oddball” cannonball shots striking about 14 percent of the time anywhere along the circle, or (2) remain stationary on most trials, and re-position to a random angle on approximately 14 percent of trials and remain stationary at this new angle. Participants receive explicit instructions regarding the nature of each of these blocks following the instructional phase, meaning they know whether the upcoming block will be from an oddball or change-point distribution. Participants complete 240 experimental trials in each condition, divided into 60-trial blocks, for a total of 480 trials. On each trial, participants adjust the position of their shield and lock in their decision. After their choice has been made, there is a 500ms delay before the location of the cannonball is revealed for 500ms. This provides an explicit representation of how far away the center of the shield fell from the cannonball’s strike, providing visual feedback about their PE from the cannonball’s actual location (Figure 2a, ii). After participants are shown how far off they were, the outcome of the task is revealed (Figure 2a, iii). After another brief delay (1000ms), the size of the participant’s shield is revealed alongside the cannonball, showing whether they successfully blocked the projectile. While the shield is always centered on the participant’s chosen location, it varies in size such that participants are never totally certain they will block the cannonball. Thus, minimizing the PE in the feedback phase is the optimal strategy for performing well in the task. At the end of a block, participants are provided feedback in terms of how many cannonballs they caught as a percentage of the total cannonballs.

Pilot Data

TL;DR for Data Cleaning

This dataframe resulting from this cleaning is an example dataset. Because the cleaning code will be updated, the current cleaning code does not need to be reviewed unless you suspect anaomalies in the resulting dataframe. For any questions related to the definition of task variables, please refer to: https://docs.google.com/spreadsheets/d/1V2UD28C_zAfH90BnNO3si6c0-qDUDFbHoU44rfdEt4I/edit#gid=1309555968. Skip to heading Pilot Results for information on pilot data.

It should also be noted that data for this pilot came from two, slightly different versions of the ADLT. The first version failed to record trial latency. Thus, latencies were only examined for a subset of participants.

1. Load Pilot Data…

####Setup - Conditionals

plot_print <- "all" # could be "random" 

1. Clean Pilot Data…

The majority of the work being done here is occuring behind the scenes in tidy_functions

2. Transform Pilot Data…

This chunk sets new variables and alters any old ones that need to be tidied. The variables created by the set_vars vector are: Abs_PE. Absolute prediction error (calculated using the discrep function): the PE from outcome to shield placement PE_angmu. Prediction error from mean of distribution to shield placement. Calculated with discrep function. catch_miss. Codes values as factors “catch”, “miss”, “noresp”. Used for coding and mean comparison changepoint A column that reflects the trial number of changepoints. contains trial number when changepoint occurs and NA when no changepoint has occured.

Table 1. High-Level Task Performance. This check returns block-level info about subject’s performance. Participants are flagged if they did not respond to more than half of trials.

3. Checks for other idiosyncracies?

It’s probably worth writing this into a function as well. I should probably do this before I implement high-level checks. Ideas for things I might need to check are: Miscodes. Misses that are actually hits, Hits that are actually misses, etc. Odd numbers of trials Participants bailing early on the task, getting stuck somewhere, etc.

cannon_data <- check_irreg(cannon_data) #check for any mistaken hits
irreg_plot_hit <- cannon_data %>% filter(grepl("CHECK_HIT", Irreg)) %>% select(subject, cond, blocknum, trialnum, catch_miss, angmu, placementAngle, outcome, Abs_PE, angleup, angledown, shield_size, Irreg )
irreg_plot_miss <- cannon_data %>% filter(grepl("CHECK_MISS", Irreg)) %>% select(subject, cond, blocknum, trialnum, catch_miss, angmu, placementAngle, outcome, Abs_PE, angleup, angledown, shield_size, Irreg )
irreg_plot_NA <- cannon_data %>% filter(grepl("CHECK_NA", Irreg)) %>% select(subject, cond, blocknum, trialnum, catch_miss, angmu, placementAngle, outcome, Abs_PE, angleup, angledown, shield_size, Irreg)

4. Create Table of Summary statistics

obj <- cannon_data %>% group_by(subject, cond, blocknum, totalearnings) %>% 
  summarise(obscount = n(), avg_PE = mean(Abs_PE, na.rm = TRUE),
            dist_mean = mean(PE_angmu,na.rm = TRUE),
            percent_caught = max(cannonballs_caught/max(trialnum)),
            percent_noresp = (sum(outcomeindex == 1)/max(trialnum))) %>%
  arrange(subject,totalearnings) %>% group_by(subject) %>% mutate(taskearnings = max(totalearnings)) %>% mutate(Avg_overall_PE = mean(avg_PE, na.rm = TRUE)) %>% ungroup()
save(obj, cannon_data, file = "Cannonball_Pilot_Example_Perfect_Data.Rdata")

colormatrix <- ifelse(obj$percent_noresp >= .2, wes_palette("Cavalcanti1")[c(1)], "white") ##potentially save these as bad_blocks vector
bad_blocks <- obj %>% dplyr::filter(percent_noresp >= .2)



  tab <- obj %>% flextable() %>% bg(j = 1:10, bg=colormatrix)


sink("/dev/null")
tab

subject	cond	blocknum	totalearnings	obscount	avg_PE	dist_mean	percent_caught	percent_noresp	taskearnings	Avg_overall_PE
364	ODDBALL	1	7.0	60	47.70814	27.373115	0.3500000	0.00000000	95.0	34.45289
364	ODDBALL	2	17.5	60	28.20098	11.497697	0.5500000	0.00000000	95.0	34.45289
364	ODDBALL	3	34.0	60	29.20829	19.296747	0.3333333	0.00000000	95.0	34.45289
364	ODDBALL	4	44.0	60	29.19695	11.981920	0.3833333	0.00000000	95.0	34.45289
364	CHANGEPOINT	1	60.0	60	35.41543	29.752510	0.4666667	0.00000000	95.0	34.45289
364	CHANGEPOINT	2	74.0	60	34.42547	27.038611	0.3333333	0.00000000	95.0	34.45289
364	CHANGEPOINT	3	84.0	60	37.20315	35.118380	0.3666667	0.00000000	95.0	34.45289
364	CHANGEPOINT	4	95.0	60	34.26472	26.664867	0.3333333	0.00000000	95.0	34.45289
400	CHANGEPOINT	1	6.0	60	34.76671	31.982583	0.4000000	0.00000000	96.5	31.76133
400	CHANGEPOINT	2	18.0	60	33.54090	31.866902	0.3500000	0.00000000	96.5	31.76133
400	CHANGEPOINT	3	28.5	60	34.41095	29.638546	0.3500000	0.00000000	96.5	31.76133
400	CHANGEPOINT	4	39.0	60	30.43282	27.020370	0.5000000	0.00000000	96.5	31.76133
400	ODDBALL	1	57.5	60	27.19565	14.791119	0.4833333	0.00000000	96.5	31.76133
400	ODDBALL	2	72.0	60	33.96318	14.135910	0.4500000	0.00000000	96.5	31.76133
400	ODDBALL	3	85.5	60	28.13848	10.921029	0.3666667	0.00000000	96.5	31.76133
400	ODDBALL	4	96.5	60	31.64196	14.181792	0.4000000	0.00000000	96.5	31.76133
455	CHANGEPOINT	1	5.5	60	42.44487	39.487840	0.4000000	0.00000000	107.5	32.26254
455	ODDBALL	1	6.0	60	25.82192	7.806403	0.6000000	0.00000000	107.5	32.26254
455	CHANGEPOINT	2	17.5	23	37.31251	28.291352	0.3684211	0.26315789	107.5	32.26254
455	CHANGEPOINT	3	17.5	1	NaN	NaN	7.0000000	1.00000000	107.5	32.26254
455	ODDBALL	2	24.0	60	30.48086	10.746947	0.5500000	0.00000000	107.5	32.26254
455	ODDBALL	3	40.5	60	37.08033	22.420291	0.3166667	0.00000000	107.5	32.26254
455	ODDBALL	4	50.0	60	33.82529	16.553933	0.4000000	0.00000000	107.5	32.26254
455	CHANGEPOINT	1	67.0	60	28.57125	21.485818	0.4000000	0.00000000	107.5	32.26254
455	CHANGEPOINT	2	79.0	60	29.41493	24.332750	0.4833333	0.00000000	107.5	32.26254
455	CHANGEPOINT	3	93.5	60	34.09523	30.436619	0.4666667	0.00000000	107.5	32.26254
455	CHANGEPOINT	4	107.5	60	23.57821	16.248584	0.4500000	0.00000000	107.5	32.26254
576	ODDBALL	1	6.0	60	46.60105	33.727181	0.2333333	0.00000000	91.0	35.00859
576	ODDBALL	2	13.0	60	29.77153	22.231551	0.3500000	0.00000000	91.0	35.00859
576	ODDBALL	3	23.5	60	37.68574	19.452241	0.3166667	0.00000000	91.0	35.00859
576	ODDBALL	4	33.0	60	29.63403	12.485928	0.5000000	0.00000000	91.0	35.00859
576	CHANGEPOINT	1	52.0	61	40.24977	32.250550	0.3166667	0.01666667	91.0	35.00859
576	CHANGEPOINT	2	61.5	60	26.41029	17.791251	0.5500000	0.00000000	91.0	35.00859
576	CHANGEPOINT	3	78.0	60	36.12394	29.554978	0.4333333	0.00000000	91.0	35.00859
576	CHANGEPOINT	4	91.0	60	33.59236	27.926018	0.4000000	0.00000000	91.0	35.00859
629	CHANGEPOINT	1	6.0	60	36.31708	34.497289	0.4000000	0.00000000	86.5	36.82969
629	CHANGEPOINT	2	18.0	60	32.65976	27.864606	0.4166667	0.00000000	86.5	36.82969
629	CHANGEPOINT	3	30.5	60	36.97084	30.933104	0.3333333	0.00000000	86.5	36.82969
629	CHANGEPOINT	4	40.5	61	31.09010	24.975045	0.3500000	0.01666667	86.5	36.82969
629	ODDBALL	1	54.5	60	55.62121	35.622707	0.2666667	0.00000000	86.5	36.82969
629	ODDBALL	2	62.5	60	34.75227	19.278573	0.4166667	0.00000000	86.5	36.82969
629	ODDBALL	3	75.0	60	36.78955	25.397277	0.3833333	0.00000000	86.5	36.82969
629	ODDBALL	4	86.5	60	30.43674	15.098659	0.5666667	0.00000000	86.5	36.82969
649	ODDBALL	1	6.0	60	27.22149	21.836616	0.4166667	0.00000000	87.0	37.35132
649	ODDBALL	2	18.5	61	46.01929	34.227465	0.3333333	0.01666667	87.0	37.35132
649	ODDBALL	3	28.5	60	37.74423	23.903617	0.4000000	0.00000000	87.0	37.35132
649	ODDBALL	4	40.5	60	40.70184	24.467198	0.3500000	0.00000000	87.0	37.35132
649	CHANGEPOINT	1	54.5	62	28.67589	25.633033	0.4500000	0.03333333	87.0	37.35132
649	CHANGEPOINT	2	68.0	60	39.40052	36.259248	0.3000000	0.00000000	87.0	37.35132
649	CHANGEPOINT	3	77.0	60	38.65265	34.136127	0.3333333	0.00000000	87.0	37.35132
649	CHANGEPOINT	4	87.0	60	40.39468	35.274746	0.3666667	0.00000000	87.0	37.35132
746	CHANGEPOINT	1	6.0	60	52.10488	46.937477	0.2000000	0.00000000	79.5	40.80936
746	CHANGEPOINT	2	12.0	60	28.76319	24.270125	0.3666667	0.00000000	79.5	40.80936
746	CHANGEPOINT	3	23.0	60	35.12056	29.125871	0.4000000	0.00000000	79.5	40.80936
746	CHANGEPOINT	4	35.0	60	37.21908	31.311846	0.4000000	0.00000000	79.5	40.80936
746	ODDBALL	1	50.5	60	43.60575	26.144958	0.2666667	0.00000000	79.5	40.80936
746	ODDBALL	2	58.5	60	38.89578	26.910302	0.3833333	0.00000000	79.5	40.80936
746	ODDBALL	3	70.0	60	43.83480	29.002452	0.3166667	0.00000000	79.5	40.80936
746	ODDBALL	4	79.5	60	46.93086	29.077890	0.4333333	0.00000000	79.5	40.80936
749	ODDBALL	1	5.5	61	51.07748	35.408264	0.2666667	0.01666667	72.0	41.29137
749	ODDBALL	2	13.5	60	47.28898	33.332059	0.2000000	0.00000000	72.0	41.29137
749	ODDBALL	3	19.5	60	34.01788	20.882086	0.3833333	0.00000000	72.0	41.29137
749	ODDBALL	4	31.0	60	44.64863	33.019453	0.3166667	0.00000000	72.0	41.29137
749	CHANGEPOINT	1	44.0	60	40.57515	38.397705	0.3000000	0.00000000	72.0	41.29137
749	CHANGEPOINT	2	53.0	60	41.21824	34.851561	0.3333333	0.00000000	72.0	41.29137
749	CHANGEPOINT	3	63.0	60	35.91092	31.390281	0.3000000	0.00000000	72.0	41.29137
749	CHANGEPOINT	4	72.0	61	35.59363	31.996868	0.3500000	0.01666667	72.0	41.29137
925	CHANGEPOINT	1	5.5	60	33.33135	29.562301	0.4000000	0.00000000	105.5	31.61699
925	CHANGEPOINT	2	17.5	60	27.74771	23.391487	0.5500000	0.00000000	105.5	31.61699
925	CHANGEPOINT	3	34.0	60	34.04666	29.792290	0.4333333	0.00000000	105.5	31.61699
925	CHANGEPOINT	4	47.0	60	31.16443	30.355131	0.4333333	0.00000000	105.5	31.61699
925	ODDBALL	1	63.5	60	23.34026	11.119006	0.5000000	0.00000000	105.5	31.61699
925	ODDBALL	2	78.5	60	29.78716	18.987555	0.5333333	0.00000000	105.5	31.61699
925	ODDBALL	3	94.5	60	33.77020	18.832966	0.3666667	0.00000000	105.5	31.61699
925	ODDBALL	4	105.5	60	39.74811	16.070638	0.4000000	0.00000000	105.5	31.61699
987	CHANGEPOINT	1	6.0	60	31.64553	24.847342	0.3666667	0.00000000	78.5	37.08251
987	CHANGEPOINT	2	17.0	60	38.64649	35.572531	0.2666667	0.00000000	78.5	37.08251
987	CHANGEPOINT	3	25.0	60	31.12037	28.738852	0.2666667	0.00000000	78.5	37.08251
987	CHANGEPOINT	4	33.0	60	29.13240	22.787501	0.4166667	0.00000000	78.5	37.08251
987	ODDBALL	1	49.0	60	36.17780	20.027808	0.3833333	0.00000000	78.5	37.08251
987	ODDBALL	2	60.5	60	44.90893	32.767667	0.2833333	0.00000000	78.5	37.08251
987	ODDBALL	3	69.0	60	43.91497	25.224111	0.3166667	0.00000000	78.5	37.08251
987	ODDBALL	4	78.5	60	41.11359	23.064621	0.2833333	0.00000000	78.5	37.08251

sink(); 

5. Reaction time data

Figure 1. Below is a histogram of all reaction time data.

Hist_outliers <- ggplot(cannon_data, aes(x=trial.placeshield_mouse.latency)) + geom_histogram()
Mean_cleaned <- cannon_data %>% ungroup () %>% filter(!trial.placeshield_mouse.latency == 2500) %>% summarise(mean = mean(trial.placeshield_mouse.latency))
Hist_outliers; Mean_cleaned

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 13 rows containing non-finite values (stat_bin).

## # A tibble: 1 x 1
##    mean
##   <dbl>
## 1  396.

6. Graphical Respresentations by subject

oddball_PE <- list();changepoint_PE <- list();oddball_DM <- list(); changepoint_DM <- list()
subjects <- as.character(unique(cannon_data$subject))
subjects_list <- list(as.character(unique(cannon_data$subject)))
for (i in subjects) {
  o <- cannon_data %>% dplyr::filter(subject == i & cond == "ODDBALL")
  oddballPE <- ggplot(o, aes(x=trialnum, y=Abs_PE, color = catch_miss)) +
     geom_point() +
     geom_vline(aes(xintercept = changepoint, color = "Oddball")) + scale_color_manual(values = (wes_palette("Cavalcanti1")[c(2,5,1,4)])) +
     xlab("Trial Number") + ylab("Response Type") + ggtitle(i) +
     facet_wrap(~blocknum)
  oddball_PE[[i]] <- oddballPE
   
  oddballDM <- ggplot(o, aes(x=trialnum, y=PE_angmu, color = catch_miss)) +
     geom_point() +
     geom_vline(aes(xintercept = changepoint, color = "Oddball")) + scale_color_manual(values = (wes_palette("Cavalcanti1")[c(2,5,1,4)])) +
     xlab("Trial Number") + ylab("Response Type") + ggtitle(i) +
     facet_wrap(~blocknum)
   oddball_DM[[i]] <- oddballDM
  
  c <- cannon_data %>% dplyr::filter(subject == i & cond == "CHANGEPOINT")
  changepointPE <- ggplot(c,aes(x=trialnum, y=Abs_PE, color = catch_miss)) + geom_point() +
     geom_vline(aes(xintercept = changepoint, color = "Changepoint")) + scale_color_manual(values = (wes_palette("Cavalcanti1")[c(4,2,5,1)]))  +
     xlab("Trial Number") + ylab("Response Type") + ggtitle(i) +
     facet_wrap(~blocknum)
  changepoint_PE[[i]] <- changepointPE
  
 changepointDM <- ggplot(c, aes(x=trialnum, y=PE_angmu, color = catch_miss)) +
   geom_point() +
   geom_vline(aes(xintercept = changepoint, color = "Changepoint")) + scale_color_manual(values = (wes_palette("Cavalcanti1")[c(4,2,1,5)]))  +
      xlab("Trial Number") + ylab("Response Type") + ggtitle(i) +
     facet_wrap(~blocknum)
 
  changepoint_DM[[i]] <- changepointDM
  
  
}
#changepoint_PE; changepoint_DM; oddball_DM; oddball_PE

Absolute PE in Oddball Block

template <- c(
    "### OddballPE {{nm}}\n",
    "```{r, echo = FALSE}\n",
    "oddball_PE[[{{nm}}]] \n",
    "```\n",
    "\n"
  )

plots <- lapply(1:length(oddball_PE), function(nm) {knitr::knit_expand(text = template)})

OddballPE 1

## Warning: Removed 205 rows containing missing values (geom_vline).

OddballPE 2

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 210 rows containing missing values (geom_vline).

OddballPE 3

## Warning: Removed 202 rows containing missing values (geom_vline).

OddballPE 4

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 207 rows containing missing values (geom_vline).

OddballPE 5

## Warning: Removed 208 rows containing missing values (geom_vline).

OddballPE 6

## Warning: Removed 202 rows containing missing values (geom_vline).

OddballPE 7

## Warning: Removed 204 rows containing missing values (geom_vline).

OddballPE 8

## Warning: Removed 206 rows containing missing values (geom_vline).

OddballPE 9

## Warning: Removed 208 rows containing missing values (geom_vline).

OddballPE 10

## Warning: Removed 204 rows containing missing values (geom_vline).

Absolute PE in Changepoint Block

template <- c(
    "### Changepoint_PE {{x}}\n",
    "```{r, echo = FALSE}\n",
    "changepoint_PE[[{{x}}]] \n",
    "```\n",
    "\n"
  )

plots <- lapply(1:length(changepoint_PE), function(x) {knitr::knit_expand(text = template)})

Changepoint_PE 1

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 273 rows containing missing values (geom_vline).

Changepoint_PE 2

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 207 rows containing missing values (geom_vline).

Changepoint_PE 3

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 211 rows containing missing values (geom_vline).

Changepoint_PE 4

## Warning: Removed 2 rows containing missing values (geom_point).

## Warning: Removed 209 rows containing missing values (geom_vline).

Changepoint_PE 5

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 204 rows containing missing values (geom_vline).

Changepoint_PE 6

## Warning: Removed 210 rows containing missing values (geom_vline).

Changepoint_PE 7

## Warning: Removed 208 rows containing missing values (geom_vline).

Changepoint_PE 8

## Warning: Removed 206 rows containing missing values (geom_vline).

Changepoint_PE 9

## Warning: Removed 205 rows containing missing values (geom_vline).

Changepoint_PE 10

## Warning: Removed 207 rows containing missing values (geom_vline).

Distance from Mean in Changepoint Block

template <- c(
    "### Changepoint_DM {{y}}\n",
    "```{r, echo = FALSE}\n",
    "changepoint_DM[[{{y}}]] \n",
    "```\n",
    "\n"
  )

plots <- lapply(1:length(changepoint_DM), function(y) {knitr::knit_expand(text = template)})

Changepoint_DM 1

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 273 rows containing missing values (geom_vline).

Changepoint_DM 2

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 207 rows containing missing values (geom_vline).

Changepoint_DM 3

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 211 rows containing missing values (geom_vline).

Changepoint_DM 4

## Warning: Removed 2 rows containing missing values (geom_point).

## Warning: Removed 209 rows containing missing values (geom_vline).

Changepoint_DM 5

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 204 rows containing missing values (geom_vline).

Changepoint_DM 6

## Warning: Removed 210 rows containing missing values (geom_vline).

Changepoint_DM 7

## Warning: Removed 208 rows containing missing values (geom_vline).

Changepoint_DM 8

## Warning: Removed 206 rows containing missing values (geom_vline).

Changepoint_DM 9

## Warning: Removed 205 rows containing missing values (geom_vline).

Changepoint_DM 10

## Warning: Removed 207 rows containing missing values (geom_vline).

Distance from Mean in Changepoint Block

template <- c(
    "### Oddball_DM {{z}}\n",
    "```{r, echo = FALSE}\n",
    "oddball_DM[[{{z}}]] \n",
    "```\n",
    "\n"
  )

plots <- lapply(1:length(oddball_DM), function(z) {knitr::knit_expand(text = template)})

Oddball_DM 1

## Warning: Removed 205 rows containing missing values (geom_vline).

Oddball_DM 2

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 210 rows containing missing values (geom_vline).

Oddball_DM 3

## Warning: Removed 202 rows containing missing values (geom_vline).

Oddball_DM 4

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 207 rows containing missing values (geom_vline).

Oddball_DM 5

## Warning: Removed 208 rows containing missing values (geom_vline).

Oddball_DM 6

## Warning: Removed 202 rows containing missing values (geom_vline).

Oddball_DM 7

## Warning: Removed 204 rows containing missing values (geom_vline).

Oddball_DM 8

## Warning: Removed 206 rows containing missing values (geom_vline).

Oddball_DM 9

## Warning: Removed 208 rows containing missing values (geom_vline).

Oddball_DM 10

## Warning: Removed 204 rows containing missing values (geom_vline).